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Record W7030108706

Media and Cultural Consumption by Young Students in the City of São Paulo, Brazil: Evidences of Digital Divide, Possibilities of Cosmopolitanism

2016· article· en· W7030108706 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCultural, Media, and Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCosmopolitanismGlobalizationContext (archaeology)Consumption (sociology)Exploratory researchPerspective (graphical)Digital media
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the initial findings of a Brazilian project, which is part of an international research group, studying youth cultures in the age of globalization. It aims to develop a comparative study from the cultural perspective of globalization on the construction of aesthetic cosmopolitanism among young people from France, Canada, Australia, and Brazil. Our aim here specifically is to understand the cultural consumption of young students from São Paulo and their uses of different media for this matter, in hybrid forms (mainly digital). The analysis of empirical data presented is built upon 52 exploratory questionnaires and 40 interviews conducted with young students (from 18 to 24 year old) living in São Paulo, Brazil. In order to understand the Brazilian context in this analysis, we performed a triangulation with secondary data from “Brazilian digital youth” by IBOPE (2012) and “Connected youth” by Telefonica Foundation/USP (2014). Based on the analysis, we reflect on two central topics: 1) evidence of a digital divide, according to their socio-economic profile and their access to information/entertainment, and 2) possibilities of cosmopolitan encounters, through the consumption of international cultural products and the search of information regarding other countries and cultures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.280
GPT teacher head0.513
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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